Neural networks are at the heart of modern artificial intelligence. Inspired by the structure of the human brain, they consist of layers of interconnected “neurons” that can learn to recognize patterns and perform complex tasks like image classification, natural language translation, and voice recognition.
A typical neural network has an input layer, one or more hidden layers, and an output layer. Each neuron processes inputs using weights and biases, applies an activation function (like ReLU or sigmoid), and passes the result to the next layer. During training, the network adjusts these weights using a process called backpropagation, which minimizes the error in predictions.
Neural networks can be categorized by structure and function:
- Feedforward Neural Networks: Data flows in one direction, commonly used for classification and regression.
- Convolutional Neural Networks (CNNs): Ideal for image processing.
- Recurrent Neural Networks (RNNs): Used for sequential data like text and time series.
Training a neural network requires a large dataset and computational power. Libraries like TensorFlow and PyTorch simplify this by providing pre-built functions and GPU support.
Neural networks are a cornerstone of deep learning, a field that has led to breakthroughs in areas such as self-driving cars, medical imaging, and AI-generated content. While they can seem complex, even a basic understanding allows developers and analysts to tap into a wide range of powerful applications.